🔥New models in 17 and 100 languages XLM/mBERT pytorch
LM supports multi-GPU and multi-node training
https://github.com/facebookresearch/XLM#pretrained-cross-lingual-language-models
LM supports multi-GPU and multi-node training
https://github.com/facebookresearch/XLM#pretrained-cross-lingual-language-models
GitHub
GitHub - facebookresearch/XLM: PyTorch original implementation of Cross-lingual Language Model Pretraining.
PyTorch original implementation of Cross-lingual Language Model Pretraining. - facebookresearch/XLM
Turbo, An Improved Rainbow Colormap for Visualization
http://ai.googleblog.com/2019/08/turbo-improved-rainbow-colormap-for.html
http://ai.googleblog.com/2019/08/turbo-improved-rainbow-colormap-for.html
research.google
Turbo, An Improved Rainbow Colormap for Visualization
Posted by Anton Mikhailov, Senior Software Engineer, Daydream False color maps show up in many applications in computer vision and machine learni...
Ludwig is a toolbox built on top of TensorFlow that allows to train and test deep learning models without the need to write code.
https://github.com/uber/ludwig
https://github.com/uber/ludwig
GitHub
GitHub - ludwig-ai/ludwig: Low-code framework for building custom LLMs, neural networks, and other AI models
Low-code framework for building custom LLMs, neural networks, and other AI models - ludwig-ai/ludwig
Data Visualization Curriculum
A data visualization curriculum of interactive notebooks, using Vega-Lite and Altair.
https://github.com/uwdata/visualization-curriculum
A data visualization curriculum of interactive notebooks, using Vega-Lite and Altair.
https://github.com/uwdata/visualization-curriculum
GitHub
GitHub - uwdata/visualization-curriculum: A data visualization curriculum of interactive notebooks.
A data visualization curriculum of interactive notebooks. - uwdata/visualization-curriculum
Deep Learning Illustrated: Building Natural Language Processing Models
https://blog.dominodatalab.com/deep-learning-illustrated-building-natural-language-processing-models/
https://blog.dominodatalab.com/deep-learning-illustrated-building-natural-language-processing-models/
domino.ai
Building Natural Language Processing Models with Keras
Deep Learning Illustrated: Building Natural Language Processing Models
TensorFlow with Apache Arrow Datasets
Apache Arrow enables the means for high-performance data exchange with TensorFlow that is both standardized and optimized for analytics and machine learning.
https://medium.com/tensorflow/tensorflow-with-apache-arrow-datasets-cdbcfe80a59f
Also TensorFlow 2.0 Release Candidate:
https://github.com/tensorflow/tensorflow/releases/tag/v2.0.0-rc0
Apache Arrow enables the means for high-performance data exchange with TensorFlow that is both standardized and optimized for analytics and machine learning.
https://medium.com/tensorflow/tensorflow-with-apache-arrow-datasets-cdbcfe80a59f
Also TensorFlow 2.0 Release Candidate:
https://github.com/tensorflow/tensorflow/releases/tag/v2.0.0-rc0
Medium
TensorFlow with Apache Arrow Datasets
An Overview of Apache Arrow Datasets Plus Example To Run Keras Model Training
Bi-Tempered Logistic Loss for Training Neural Nets with Noisy Data
http://ai.googleblog.com/2019/08/bi-tempered-logistic-loss-for-training.html
http://ai.googleblog.com/2019/08/bi-tempered-logistic-loss-for-training.html
blog.research.google
Bi-Tempered Logistic Loss for Training Neural Nets with Noisy Data
Exploring Weight Agnostic Neural Networks
article: http://ai.googleblog.com/2019/08/exploring-weight-agnostic-neural.html
habr: https://habr.com/ru/post/465369/
article: http://ai.googleblog.com/2019/08/exploring-weight-agnostic-neural.html
habr: https://habr.com/ru/post/465369/
blog.research.google
Exploring Weight Agnostic Neural Networks
This is an attempt to modify Dive into Deep Learning, Berkeley STAT 157 (Spring 2019) textbook's code into PyTorch.
https://github.com/dsgiitr/d2l-pytorch
https://github.com/dsgiitr/d2l-pytorch
GitHub
GitHub - dsgiitr/d2l-pytorch: This project reproduces the book Dive Into Deep Learning (https://d2l.ai/), adapting the code from…
This project reproduces the book Dive Into Deep Learning (https://d2l.ai/), adapting the code from MXNet into PyTorch. - dsgiitr/d2l-pytorch
Forwarded from Artificial Intelligence
How to Implement the Inception Score (IS) for Evaluating GANs
https://machinelearningmastery.com/how-to-implement-the-inception-score-from-scratch-for-evaluating-generated-images/
https://machinelearningmastery.com/how-to-implement-the-inception-score-from-scratch-for-evaluating-generated-images/
🔥AI For Everyone Free course from Andrew Ng
In this course, you will learn:
- The meaning behind common AI terminology, including neural networks, machine learning, deep learning, and data science
- What AI realistically can--and cannot--do
- How to spot opportunities to apply AI to problems in your own organization
- What it feels like to build machine learning and data science projects
- How to work with an AI team and build an AI strategy in your company
- How to navigate ethical and societal discussions surrounding AI
https://www.coursera.org/learn/ai-for-everyone
In this course, you will learn:
- The meaning behind common AI terminology, including neural networks, machine learning, deep learning, and data science
- What AI realistically can--and cannot--do
- How to spot opportunities to apply AI to problems in your own organization
- What it feels like to build machine learning and data science projects
- How to work with an AI team and build an AI strategy in your company
- How to navigate ethical and societal discussions surrounding AI
https://www.coursera.org/learn/ai-for-everyone
Coursera
AI For Everyone
Offered by DeepLearning.AI. AI is not only for ... Enroll for free.
PyTorch Examples
A repository showcasing examples of using PyTorch
https://github.com/pytorch/examples
A repository showcasing examples of using PyTorch
https://github.com/pytorch/examples
GitHub
GitHub - pytorch/examples: A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.
A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc. - pytorch/examples
🚀 Introducing TF-GAN: A lightweight GAN library for TensorFlow 2.0
Tensorflow blog: https://medium.com/tensorflow/introducing-tf-gan-a-lightweight-gan-library-for-tensorflow-2-0-36d767e1abae
Code: https://github.com/tensorflow/gan
Free course: https://developers.google.com/machine-learning/gan/
Paper: https://arxiv.org/abs/1805.08318
Tensorflow blog: https://medium.com/tensorflow/introducing-tf-gan-a-lightweight-gan-library-for-tensorflow-2-0-36d767e1abae
Code: https://github.com/tensorflow/gan
Free course: https://developers.google.com/machine-learning/gan/
Paper: https://arxiv.org/abs/1805.08318
Medium
Introducing TF-GAN: A lightweight GAN library for TensorFlow 2.0
Posted by Joel Shor, Yoel Drori, Google Research Tel Aviv, Aaron Sarna, David Westbrook, Paige Bailey
🔥Finally, AI-Based Painting is here!
#GANPaint
video: https://www.youtube.com/watch?v=IqHs_DkmDVo
Semantic Photo Manipulation with a Generative Image Prior
paper: http://ganpaint.io/
#GANPaint
video: https://www.youtube.com/watch?v=IqHs_DkmDVo
Semantic Photo Manipulation with a Generative Image Prior
paper: http://ganpaint.io/
YouTube
Finally, AI-Based Painting is Here!
❤️ Check out Weights & Biases here and sign up for a free demo: https://www.wandb.com/papers
📝 The paper "GANPaint Studio - Semantic Photo Manipulation with a Generative Image Prior" and its online demo are available here:
http://ganpaint.io/
🙏 We would…
📝 The paper "GANPaint Studio - Semantic Photo Manipulation with a Generative Image Prior" and its online demo are available here:
http://ganpaint.io/
🙏 We would…
👍1
A Gentle Introduction to Generative Adversarial Network Loss Functions
https://machinelearningmastery.com/generative-adversarial-network-loss-functions/
https://machinelearningmastery.com/generative-adversarial-network-loss-functions/
Adapt or Get Left Behind: Domain Adaptation through BERT Language Model Finetuning for Aspect-Target Sentiment Classification
https://arxiv.org/abs/1908.11860
https://arxiv.org/abs/1908.11860
❤1
Rules of Machine Learning by Google
Best Practices for ML Engineering
https://developers.google.com/machine-learning/guides/rules-of-ml/
Best Practices for ML Engineering
https://developers.google.com/machine-learning/guides/rules-of-ml/
Google for Developers
Rules of Machine Learning: | Google for Developers
A Gentle Introduction to Jensen’s Inequality
https://machinelearningmastery.com/a-gentle-introduction-to-jensens-inequality/
https://machinelearningmastery.com/a-gentle-introduction-to-jensens-inequality/
MachineLearningMastery.com
A Gentle Introduction to Jensen’s Inequality - MachineLearningMastery.com
It is common in statistics and machine learning to create a linear transform or mapping of a variable. An example is a linear scaling of a feature variable. We have the natural intuition that the mean of the scaled values is the same as the scaled value of…